Bias in Data Collection
Andrew discusses the critical impact of biased data collection on statistical inference and machine learning outcomes. He highlights the interplay between empirical work and theoretical frameworks, shedding light on the challenges researchers face when addressing data bias. This conversation offers valuable insights for anyone navigating the complexities of machine learning methodologies.In this clip
From this podcast
Machine Learning Street Talk (MLST)
Adversarial Examples and Data Modelling - Andrew Ilyas (MIT)
Related Questions
Are there biases in AI?
Do you have any tips for analyzing data objectively and avoiding bias toward my own theory in private research involving statistical analysis?
Are there biases in AI as discussed in the episode Yoshua Bengio: The Past, Present, and Future of Deep Learning and the clip General Inductive Biases?